Phase II, randomised, double‐blind, placebo‐controlled trial of methylphenidate for reduction of fatigue levels in patients with prostate cancer receiving <scp>LHRH</scp> ‐agonist therapy
Bibliographic record
Abstract
OBJECTIVES: To investigate whether methylphenidate can alleviate fatigue, as measured by the Functional Assessment of Cancer Therapy: Fatigue subscale, in men with prostate cancer (PCa) treated with a luteinizing hormone-releasing hormone (LHRH) for a minimum of 6 months, and to assess changes in global fatigue and quality of life (QoL) as measured by the Bruera Global Fatigue Severity Scale (BFS) and the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), respectively. PARTICIPANTS AND METHODS: We performed a single-centre, randomised, double-blind, placebo-controlled trial with the aim of recruiting 128 participants. Men treated with a LHRH agonist for PCa were screened between February 2008 and June 2012 for fatigue at our outpatient clinics using the BFS. Participants were randomised to receive either 10 mg daily of methylphenidate or placebo. Change in fatigue levels and in SF-36 scores between both groups were compared using linear regression, adjusted for baseline scores. RESULTS: The study was closed prematurely because of poor accrual. Of the 790 subjects screened, 24 men were randomised to methylphenidate or placebo (12 per group). After 10 weeks, the improvement in mean [sd] fatigue score was greater in the methylphenidate than in the placebo arm (+7.7 [7.7] vs +1.4 [7.6]; P = 0.022). The within-group analysis showed a significant improvement in fatigue scores in the methylphenidate arm (P = 0.008) but not in the placebo arm (P = 0.82). The use of methylphenidate also resulted in a significantly greater improvement in QoL as measured by the physical and mental component summary scores than did the use of placebo (P = 0.04 for both component scores). CONCLUSIONS: Our findings support the beneficial effect of methylphenidate on fatigue and QoL among men with LHRH-induced fatigue. Clinicians should be aware of these benefits and should consider discussing these findings with patients who have high levels of fatigue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".